Abstract
Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission.
Highlights
Large data centers are nowadays an integral part of the information technology (IT) industry
We aim to reduce the cost associated with the data center based on optimal selection of data center considering the nature of energy source, carbon emission, carbon tax and energy price while satisfying the user requests
To check the competence of different algorithms on VM to physical machine (PM) mapping, instruction to total energy ratio (IER), instruction to carbon footprint ratio (ICFR), instruction to cost ratio (ICR) and ratio of VM acceptance (RVA) measures are calculated based on the results summarized in Table 8 using Equations (12)–(14)
Summary
Large data centers are nowadays an integral part of the information technology (IT) industry. Due to increasing power densityheat and thermal management are crucial for data centers to increase the lifetime of the servers and to reduce economic loss in the form of electricity bill. The two possible ways to overcome the problem of CO2 emission are (1) grid power source to be replaced with renewable energy sources; (2) Improve the Power Usage Effectiveness (PUE) of the data centers. The Green Grid consortium [4] defines the PUE metric as the ratio between the total power consumed by the data center (IT power + overhead power) and energy consumed by servers executing IT load (IT power). A game-based thermal-aware resource allocation was proposed in [8] It uses a cooperative Nash-bargaining solution to reduce the thermal imbalance in data centers. Thermal management is proposed to distribute the load at the rack level to handle temperature drop effectively but fails to handle hotspots [10]
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